Artificial Intelligence Nanodegree

Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.


Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 6: Write your Algorithm
  • Step 7: Test Your Algorithm

Step 0: Import Datasets

Import Dog Dataset

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

  • train_files, valid_files, test_files - numpy arrays containing file paths to images
  • train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
  • dog_names - list of string-valued dog breed names for translating labels
In [1]:
from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('/data/dog_images/train')
valid_files, valid_targets = load_dataset('/data/dog_images/valid')
test_files, test_targets = load_dataset('/data/dog_images/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("/data/dog_images/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
Using TensorFlow backend.
There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

Import Human Dataset

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.

In [2]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("/data/lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
There are 13233 total human images.

Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [3]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [4]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

  • 100% of the human images have a detected face.
  • 11% of the dog images have a detected face.
In [5]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.

num_faces_human = 0;
for human_file in human_files_short:
    if face_detector(human_file):
        num_faces_human += 1

num_faces_dog = 0;
for dog_file in dog_files_short:
    if face_detector(dog_file):
        num_faces_dog += 1

human_detected_face_percentage = 100 * num_faces_human / len(human_files_short)
dog_detected_face_percentage = 100 * num_faces_dog / len(dog_files_short)

print(f'Percentage of human images with a detected face: {human_detected_face_percentage}%')
print(f'Percentage of dog images with a detected face: {dog_detected_face_percentage}%')
Percentage of human images with a detected face: 100.0%
Percentage of dog images with a detected face: 11.0%

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

Answer:

I do not think it is reasonable to communicate to the user that human images must provide a clear view of a face. Instead, we could train a CNN to recognize the silhouette of a human (head, torso, two arms, two legs) and / or other distinguishing features such as hair.

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.

In [6]:
## (Optional) TODO: Report the performance of another  
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.

Step 2: Detect Dogs

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

In [7]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5
102858752/102853048 [==============================] - 3s 0us/step

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

In [8]:
from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

In [9]:
from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [10]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

(IMPLEMENTATION) Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

  • 0% of the human images have a detected dog.
  • 100% of the dog images have a detected dog.
In [11]:
### Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.

num_dog_human = 0;
for human_file in human_files_short:
    if dog_detector(human_file):
        num_dog_human += 1

num_dog_dog = 0;
for dog_file in dog_files_short:
    if dog_detector(dog_file):
        num_dog_dog += 1
        
human_detected_dog_percentage = 100 * num_dog_human / len(human_files_short)
dog_detected_dog_percentage = 100 * num_dog_dog / len(dog_files_short)

print(f'Percentage of human images with a detected dog: {human_detected_dog_percentage}%')
print(f'Percentage of dog images with a detected dog: {dog_detected_dog_percentage}%')
Percentage of human images with a detected dog: 0.0%
Percentage of dog images with a detected dog: 100.0%

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

In [12]:
from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
100%|██████████| 6680/6680 [01:15<00:00, 88.66it/s] 
100%|██████████| 835/835 [00:08<00:00, 99.49it/s] 
100%|██████████| 836/836 [00:08<00:00, 99.73it/s] 

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Sample CNN

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer:

As per Alexis Cook's suggestion, we design a CNN architecture with the goal of taking an input array (the image) and gradually making it deeper than it is tall or wide. This will transform the data from a representation of spacial information to a representation of content information. To do this we do the following:

First, we use a series of convolutional layers, where the number of filters in each layer grows exponentially (16, 32, 64). We set the stride to 'one', padding to 'same' so that the width and height of our array remains the same in each layer. This process gives us a method for gradually increasing the depth of our array without changing its width and height.

Second, we use a series of max pooling layers interspersed with our convolutional layers, each with a pool_size of two. This has the effect of halving the spacial dimensions of the previous layer. By doing this, we are making our content deeper than it is tall or wide.

Last, we flatten our array into a vector and feed it into a fully connected layer to determine which dog breed is most probable. We achieve this using a Dense layer with 133 units (one for each dog breed) and a 'softmax' Activation function to do a multi-class prediction. Prior to applying each activation layer, we add a batch normalization layer to normalize the input into the activation function.

In [13]:
from keras.layers import (Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dropout,
                          Flatten, Dense, Activation, BatchNormalization)
from keras.models import Sequential

model = Sequential()

### Define your architecture.
model.add(Conv2D(filters=16, kernel_size=2, activation='relu', input_shape=train_tensors.shape[1:]))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(532))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(133))
model.add(BatchNormalization())
model.add(Activation('softmax'))

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 223, 223, 16)      208       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 111, 111, 16)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 111, 111, 32)      2080      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 55, 55, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 55, 55, 64)        8256      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 27, 27, 64)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 27, 27, 64)        0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 46656)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 532)               24821524  
_________________________________________________________________
batch_normalization_1 (Batch (None, 532)               2128      
_________________________________________________________________
activation_50 (Activation)   (None, 532)               0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 532)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 133)               70889     
_________________________________________________________________
batch_normalization_2 (Batch (None, 133)               532       
_________________________________________________________________
activation_51 (Activation)   (None, 133)               0         
=================================================================
Total params: 24,905,617
Trainable params: 24,904,287
Non-trainable params: 1,330
_________________________________________________________________

Compile the Model

In [14]:
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [15]:
from keras.callbacks import ModelCheckpoint

### Specify the number of epochs that you would like to use to train the model.

epochs = 20

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6660/6680 [============================>.] - ETA: 0s - loss: 4.8108 - acc: 0.0264Epoch 00001: val_loss improved from inf to 4.58126, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 31s 5ms/step - loss: 4.8101 - acc: 0.0263 - val_loss: 4.5813 - val_acc: 0.0515
Epoch 2/20
6660/6680 [============================>.] - ETA: 0s - loss: 4.2197 - acc: 0.1071Epoch 00002: val_loss improved from 4.58126 to 4.40482, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 4.2195 - acc: 0.1070 - val_loss: 4.4048 - val_acc: 0.0731
Epoch 3/20
6660/6680 [============================>.] - ETA: 0s - loss: 3.8108 - acc: 0.2488Epoch 00003: val_loss improved from 4.40482 to 4.40148, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 3.8115 - acc: 0.2482 - val_loss: 4.4015 - val_acc: 0.0826
Epoch 4/20
6660/6680 [============================>.] - ETA: 0s - loss: 3.6010 - acc: 0.3706Epoch 00004: val_loss improved from 4.40148 to 4.38078, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 3.6013 - acc: 0.3699 - val_loss: 4.3808 - val_acc: 0.0826
Epoch 5/20
6660/6680 [============================>.] - ETA: 0s - loss: 3.5029 - acc: 0.4410Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 30s 4ms/step - loss: 3.5030 - acc: 0.4406 - val_loss: 4.3923 - val_acc: 0.0671
Epoch 6/20
6660/6680 [============================>.] - ETA: 0s - loss: 3.3967 - acc: 0.4769Epoch 00006: val_loss improved from 4.38078 to 4.35842, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 3.3970 - acc: 0.4763 - val_loss: 4.3584 - val_acc: 0.0910
Epoch 7/20
6660/6680 [============================>.] - ETA: 0s - loss: 3.2489 - acc: 0.5221Epoch 00007: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 3.2482 - acc: 0.5222 - val_loss: 4.3999 - val_acc: 0.0934
Epoch 8/20
6660/6680 [============================>.] - ETA: 0s - loss: 3.1301 - acc: 0.5613Epoch 00008: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 3.1312 - acc: 0.5605 - val_loss: 4.3588 - val_acc: 0.0946
Epoch 9/20
6660/6680 [============================>.] - ETA: 0s - loss: 3.0000 - acc: 0.6014Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 2.9997 - acc: 0.6015 - val_loss: 4.3684 - val_acc: 0.1018
Epoch 10/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.8624 - acc: 0.6341Epoch 00010: val_loss improved from 4.35842 to 4.33024, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 2.8635 - acc: 0.6338 - val_loss: 4.3302 - val_acc: 0.0946
Epoch 11/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.7544 - acc: 0.6592Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 2.7540 - acc: 0.6593 - val_loss: 4.3766 - val_acc: 0.0934
Epoch 12/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.6245 - acc: 0.6952Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 2.6261 - acc: 0.6948 - val_loss: 4.3717 - val_acc: 0.0754
Epoch 13/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.5076 - acc: 0.7122Epoch 00013: val_loss improved from 4.33024 to 4.32987, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 2.5072 - acc: 0.7120 - val_loss: 4.3299 - val_acc: 0.1018
Epoch 14/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.3939 - acc: 0.7444Epoch 00014: val_loss improved from 4.32987 to 4.32152, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 2.3941 - acc: 0.7446 - val_loss: 4.3215 - val_acc: 0.1030
Epoch 15/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.2330 - acc: 0.7673Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 2.2331 - acc: 0.7674 - val_loss: 4.3240 - val_acc: 0.0970
Epoch 16/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.1305 - acc: 0.7881Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 2.1302 - acc: 0.7882 - val_loss: 4.3281 - val_acc: 0.0958
Epoch 17/20
6660/6680 [============================>.] - ETA: 0s - loss: 2.0172 - acc: 0.8123Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 30s 4ms/step - loss: 2.0171 - acc: 0.8129 - val_loss: 4.3286 - val_acc: 0.0886
Epoch 18/20
6660/6680 [============================>.] - ETA: 0s - loss: 1.9062 - acc: 0.8384Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 30s 5ms/step - loss: 1.9059 - acc: 0.8386 - val_loss: 4.3236 - val_acc: 0.0970
Epoch 19/20
6660/6680 [============================>.] - ETA: 0s - loss: 1.7730 - acc: 0.8512Epoch 00019: val_loss improved from 4.32152 to 4.31282, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 5ms/step - loss: 1.7714 - acc: 0.8516 - val_loss: 4.3128 - val_acc: 0.0790
Epoch 20/20
6660/6680 [============================>.] - ETA: 0s - loss: 1.6684 - acc: 0.8682Epoch 00020: val_loss did not improve
6680/6680 [==============================] - 30s 4ms/step - loss: 1.6681 - acc: 0.8680 - val_loss: 4.3657 - val_acc: 0.0850
Out[15]:
<keras.callbacks.History at 0x7f5905405320>

Load the Model with the Best Validation Loss

In [16]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

In [17]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 9.6890%

Step 4: Use a CNN to Classify Dog Breeds

To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.

Obtain Bottleneck Features

In [18]:
bottleneck_features = np.load('/data/bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

In [19]:
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_1 ( (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________

Compile the Model

In [20]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

In [21]:
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6500/6680 [============================>.] - ETA: 0s - loss: 12.5189 - acc: 0.1132Epoch 00001: val_loss improved from inf to 10.91238, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 327us/step - loss: 12.4948 - acc: 0.1150 - val_loss: 10.9124 - val_acc: 0.1940
Epoch 2/20
6460/6680 [============================>.] - ETA: 0s - loss: 10.0967 - acc: 0.2807Epoch 00002: val_loss improved from 10.91238 to 9.94568, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 259us/step - loss: 10.1056 - acc: 0.2813 - val_loss: 9.9457 - val_acc: 0.2946
Epoch 3/20
6520/6680 [============================>.] - ETA: 0s - loss: 9.5440 - acc: 0.3449Epoch 00003: val_loss improved from 9.94568 to 9.67600, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 252us/step - loss: 9.5071 - acc: 0.3464 - val_loss: 9.6760 - val_acc: 0.3186
Epoch 4/20
6640/6680 [============================>.] - ETA: 0s - loss: 9.2318 - acc: 0.3782Epoch 00004: val_loss improved from 9.67600 to 9.54935, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 251us/step - loss: 9.2417 - acc: 0.3778 - val_loss: 9.5493 - val_acc: 0.3413
Epoch 5/20
6520/6680 [============================>.] - ETA: 0s - loss: 9.1107 - acc: 0.4038Epoch 00005: val_loss improved from 9.54935 to 9.51004, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 252us/step - loss: 9.1098 - acc: 0.4033 - val_loss: 9.5100 - val_acc: 0.3449
Epoch 6/20
6560/6680 [============================>.] - ETA: 0s - loss: 9.0070 - acc: 0.4142Epoch 00006: val_loss improved from 9.51004 to 9.31642, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 251us/step - loss: 9.0080 - acc: 0.4141 - val_loss: 9.3164 - val_acc: 0.3653
Epoch 7/20
6580/6680 [============================>.] - ETA: 0s - loss: 8.9119 - acc: 0.4242Epoch 00007: val_loss did not improve
6680/6680 [==============================] - 2s 249us/step - loss: 8.9151 - acc: 0.4237 - val_loss: 9.3300 - val_acc: 0.3545
Epoch 8/20
6560/6680 [============================>.] - ETA: 0s - loss: 8.7943 - acc: 0.4351Epoch 00008: val_loss improved from 9.31642 to 9.14238, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 250us/step - loss: 8.8115 - acc: 0.4338 - val_loss: 9.1424 - val_acc: 0.3593
Epoch 9/20
6600/6680 [============================>.] - ETA: 0s - loss: 8.7051 - acc: 0.4456Epoch 00009: val_loss improved from 9.14238 to 9.14016, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 252us/step - loss: 8.7123 - acc: 0.4451 - val_loss: 9.1402 - val_acc: 0.3725
Epoch 10/20
6540/6680 [============================>.] - ETA: 0s - loss: 8.6565 - acc: 0.4479Epoch 00010: val_loss improved from 9.14016 to 8.98507, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 248us/step - loss: 8.6462 - acc: 0.4485 - val_loss: 8.9851 - val_acc: 0.3808
Epoch 11/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.5172 - acc: 0.4611Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 2s 251us/step - loss: 8.5296 - acc: 0.4600 - val_loss: 8.9886 - val_acc: 0.3844
Epoch 12/20
6460/6680 [============================>.] - ETA: 0s - loss: 8.4825 - acc: 0.4630Epoch 00012: val_loss improved from 8.98507 to 8.94891, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 248us/step - loss: 8.5025 - acc: 0.4614 - val_loss: 8.9489 - val_acc: 0.3808
Epoch 13/20
6520/6680 [============================>.] - ETA: 0s - loss: 8.3744 - acc: 0.4667Epoch 00013: val_loss improved from 8.94891 to 8.85590, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 256us/step - loss: 8.4103 - acc: 0.4645 - val_loss: 8.8559 - val_acc: 0.3892
Epoch 14/20
6620/6680 [============================>.] - ETA: 0s - loss: 8.2796 - acc: 0.4731Epoch 00014: val_loss improved from 8.85590 to 8.77256, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 251us/step - loss: 8.2936 - acc: 0.4723 - val_loss: 8.7726 - val_acc: 0.3964
Epoch 15/20
6660/6680 [============================>.] - ETA: 0s - loss: 8.1846 - acc: 0.4815Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 2s 257us/step - loss: 8.1850 - acc: 0.4814 - val_loss: 8.7753 - val_acc: 0.3928
Epoch 16/20
6640/6680 [============================>.] - ETA: 0s - loss: 8.0095 - acc: 0.4905Epoch 00016: val_loss improved from 8.77256 to 8.62043, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 249us/step - loss: 8.0315 - acc: 0.4892 - val_loss: 8.6204 - val_acc: 0.4036
Epoch 17/20
6620/6680 [============================>.] - ETA: 0s - loss: 7.8358 - acc: 0.4991Epoch 00017: val_loss improved from 8.62043 to 8.37424, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 257us/step - loss: 7.8319 - acc: 0.4993 - val_loss: 8.3742 - val_acc: 0.4036
Epoch 18/20
6500/6680 [============================>.] - ETA: 0s - loss: 7.6964 - acc: 0.5114Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 2s 246us/step - loss: 7.6784 - acc: 0.5124 - val_loss: 8.4388 - val_acc: 0.4144
Epoch 19/20
6480/6680 [============================>.] - ETA: 0s - loss: 7.6578 - acc: 0.5173Epoch 00019: val_loss improved from 8.37424 to 8.24064, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 255us/step - loss: 7.6545 - acc: 0.5174 - val_loss: 8.2406 - val_acc: 0.4371
Epoch 20/20
6480/6680 [============================>.] - ETA: 0s - loss: 7.6498 - acc: 0.5213Epoch 00020: val_loss did not improve
6680/6680 [==============================] - 2s 253us/step - loss: 7.6372 - acc: 0.5222 - val_loss: 8.3507 - val_acc: 0.4192
Out[21]:
<keras.callbacks.History at 0x7f590c16ab00>

Load the Model with the Best Validation Loss

In [22]:
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

In [23]:
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 44.1388%

Predict Dog Breed with the Model

In [24]:
from extract_bottleneck_features import *

def VGG16_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = VGG16_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras. These are already in the workspace, at /data/bottleneck_features. If you wish to download them on a different machine, they can be found at:

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception.

The above architectures are downloaded and stored for you in the /data/bottleneck_features/ folder.

This means the following will be in the /data/bottleneck_features/ folder:

DogVGG19Data.npz DogResnet50Data.npz DogInceptionV3Data.npz DogXceptionData.npz

(IMPLEMENTATION) Obtain Bottleneck Features

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
In [25]:
### Obtain bottleneck features from another pre-trained CNN.

bottleneck_features = np.load('/data/bottleneck_features/DogInceptionV3Data.npz')
train_Inception = bottleneck_features['train']
valid_Inception = bottleneck_features['valid']
test_Inception = bottleneck_features['test']

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    <your model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

We use bottleneck features from the Inception pre-trained model to create a new CNN. The train bottleneck features are provided as the input shape to a GlobalAveragePooling2D layer. The output is fed into a fully connected layer (again with 133 units, one for each of our dog breeds) and a 'softmax' activation to do the multi-class prediction.

In [26]:
### Define your architecture.
Inception_model = Sequential()
Inception_model.add(GlobalAveragePooling2D(input_shape=train_Inception.shape[1:]))
Inception_model.add(Dense(133, activation='softmax'))

Inception_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 133)               272517    
=================================================================
Total params: 272,517
Trainable params: 272,517
Non-trainable params: 0
_________________________________________________________________

(IMPLEMENTATION) Compile the Model

In [27]:
### Compile the model.

Inception_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [28]:
### Train the model.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Inception.hdf5', 
                               verbose=1, save_best_only=True)

Inception_model.fit(train_Inception, train_targets, 
          validation_data=(valid_Inception, valid_targets),
          epochs=1, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/1
6620/6680 [============================>.] - ETA: 0s - loss: 1.1484 - acc: 0.7053Epoch 00001: val_loss improved from inf to 0.61832, saving model to saved_models/weights.best.Inception.hdf5
6680/6680 [==============================] - 3s 402us/step - loss: 1.1473 - acc: 0.7051 - val_loss: 0.6183 - val_acc: 0.8263
Out[28]:
<keras.callbacks.History at 0x7f590c2a1da0>

(IMPLEMENTATION) Load the Model with the Best Validation Loss

In [29]:
### Load the model weights with the best validation loss.

Inception_model.load_weights('saved_models/weights.best.Inception.hdf5')

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.

In [30]:
### Calculate classification accuracy on the test dataset.

# Get index of predicted dog breed for each image in test set
Inception_predictions = [np.argmax(Inception_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Inception]

# Report test accuracy
test_accuracy = 100*np.sum(np.array(Inception_predictions)==np.argmax(test_targets, axis=1))/len(Inception_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 78.1100%

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

  1. Extract the bottleneck features corresponding to the chosen CNN model.
  2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
  3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

In [31]:
### Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.

def Inception_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = Inception_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 6: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [73]:
import re
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Pretty display for notebooks
%matplotlib notebook

def parse_dog_name(dog_name):
    return re.sub(r'_', ' ', re.sub(r'in\/[0-9]*\.', '', dog_name));

### Write your algorithm.
### Feel free to use as many code cells as needed.

def dog_predictor(img_path):
    img_rgb=mpimg.imread(img_path)
    if img_rgb is None: 
        raise Exception("could not load image!")
    if face_detector(img_path):
        predicted_breed = Inception_predict_breed(img_path)
        print(f'Hello human, you look like a {parse_dog_name(predicted_breed)}')
    elif dog_detector(img_path):
        predicted_breed = Inception_predict_breed(img_path)
        print(f'Hello dog, I think you are a {parse_dog_name(predicted_breed)}')
    else:
        print('Error: no human or dog detected!')
    plt.figure(img_path)
    img=mpimg.imread(img_path)
    imgplot = plt.imshow(img)

Step 7: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer:

The model recognized that I am a human and "predicted" that I look like a Borzoi. It also recognized that my wife is a human and "predicted" she looks like a Petit Basset Griffon Vendeen. It did not recognize my cat as a human or a dog. It recognized my family's Greyhound as an Italian Greyhound which strictly is a failure because our labeled dog data includes both breeds. Subjectively though, it is still pretty good as Greyhounds and Italian Greyhounds are closely related. The model also recognized a picture of a Corgi as a Corgi.

It recognized a Corgi-German Shepherd mix as a German Shepherd, not a Corgi. There is a quote, "Fascinating at how crossing a Corgi with any other dog breed results in what is basically a Corgi in disguise as the other breed". A more sophisticated would not get fooled by the "disguise". If we needed to recognize hybrids, we could extend our labeled dog data to include labeled images of hybrid breeds and train it to recognize these too.

Another area of improvement for the algorithm would be to recognize humans even when there is not a detected face in the image. As discussed earlier, this could be achieved by training the CNN to recognize the silhouette of a human or other human features such as hair.

Lastly the algorithm is still somewhat slow in predicting dog breeds. It takes on the order of seconds to make a prediction for each image. Part of the problem is that we are making two predictions sequentially. First we attempt to detect a face and then we attempt to detect a dog. Instead, we could use one model that detected both humans and dogs. If Human was the most probable classifier, we could then look for the most probable dog breed classifier. If a dog breed was the most probable classifier, we simple would return that prediction. This would allow us to run only one model which would be more efficient.

In [80]:
## Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.

image_names = ['human', 'human2', 'cat', 'sparky', 'corgi', 'corgi-mix']

for image_name in image_names:
    dog_predictor(f'uploads/{image_name}.jpg')
Hello human, you look like a Borzoi
Hello human, you look like a Petit basset griffon vendeen
Error: no human or dog detected!
Hello dog, I think you are a Italian greyhound
Hello dog, I think you are a Pembroke welsh corgi
Hello dog, I think you are a German shepherd dog

Please download your notebook to submit

In order to submit, please do the following:

  1. Download an HTML version of the notebook to your computer using 'File: Download as...'
  2. Click on the orange Jupyter circle on the top left of the workspace.
  3. Navigate into the dog-project folder to ensure that you are using the provided dog_images, lfw, and bottleneck_features folders; this means that those folders will not appear in the dog-project folder. If they do appear because you downloaded them, delete them.
  4. While in the dog-project folder, upload the HTML version of this notebook you just downloaded. The upload button is on the top right.
  5. Navigate back to the home folder by clicking on the two dots next to the folder icon, and then open up a terminal under the 'new' tab on the top right
  6. Zip the dog-project folder with the following command in the terminal: zip -r dog-project.zip dog-project
  7. Download the zip file by clicking on the square next to it and selecting 'download'. This will be the zip file you turn in on the next node after this workspace!